Automated Scoring System Using Dependency-Based Weighted Semantic Similarity Model

Traditionally, automated scoring system uses semantic similarity between words and the weight of words to calculate semantic similarity between student´s answer and standard answer. It doesn´t consider the word-order or syntactic information, which can improve the knowledge representation and therefore lead to better performance. This article presents a novel approach called dependency-based weighted semantic similarity model which takes syntactic relations into account and incorporates word-based information in addition to dependency parsing. The experiment shows that compared with traditional word-based weighted semantic similarity model, the dependency-based weighted semantic similarity model improves the precision obviously. It also provides better discrimination of syntactic-semantic knowledge representation than the traditional one.